Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries

Seol, Jae Wook; Yi, Won-Jin; Choi, Jinwook; Lee, Kyung Soon

doi:10.1016/j.ijmedinf.2016.10.021

Cited by 18 publications

(16 citation statements)

References 21 publications

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“…A chronological view of a patient's history makes clinical audits easier and improves quality of care. Seol et al [ 26 ] proposed a method to extract clinical events related to a patient using conditional random fields and to extract relationships between events using support vector machines and to extract event causality pattern list in a semantic unit of events. The categories of events extracted are symptom, diagnosis, drug, treatment, purpose, test, finding, time, and visit.…”

Section: Information Extraction Of Emr Based On Text Miningmentioning

confidence: 99%

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

Sun

Cai

et al. 2018

Journal of Healthcare Engineering

222

102

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Currently, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work.

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Section: Information Extraction Of Emr Based On Text Miningmentioning

confidence: 99%

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

Sun

Cai

et al. 2018

Journal of Healthcare Engineering

222

102

View full text Add to dashboard Cite

show abstract

“…Due to the unstructured nature, most work utilize the statistical machine learning methods. For example, Seol et al [10] proposed a clinical Problem-Action relation extraction framework based on CRF and Support Vector Machine(SVM). Skeppstedt et al [11] studied the usefulness of features extracted from unsupervised methods and applied them in clinical named entity recognition problem.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Information Extraction Framework on Chinese Electronic Health Records

Tian

Zhang²,

Liu

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

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Electronic Health Records (EHRs) store a large amount of clinical data associated with each patient. Information extraction on unstructured clinical notes in EHRs is important which could contribute to huge improvement in patient health management. Previous studies mainly focused on English corpus. However, at the same time there are very limited research work on Chinese EHRs. Due to the challenges brought by the characteristics of Chinese, it is difficult to apply existing techniques for English on Chinese corpus. In this paper, we propose a deep learning based framework for information extraction from clinical notes in Chinese EHRs. Our framework consists of three components: data preprocessing, feature generation and entity and relation extractor. For clinical entity recognition, we propose a novel Conditional Random Field (CRF) based model and introduce effective features by leveraging the characteristics of Chinese language. For relation extraction, we utilize Convolutional Neural Network CNN to obtain high quality entity-relation facts. To the best of our knowledge, this is the first framework to apply deep learning to information extraction from clinical notes in Chinese EHRs. We conduct extensive sets of experiments on real-world datasets from hospital. The experimental results show the effectiveness of our framework, indicating its practical application value.

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“…Detection System by KDD'99 data set [178], question answering [138], air transport safty (Netherland) [144], Detecting Network Neutrality violations [143], gene regulatory network [157][158][159][160], gene-gene and gene-environment interaction [166],cancer diagnosis in breast cancer study [180,181], cancer subgroup mining with heterogeneous treatment causal effects [148], identify semantic relations in text [165,176],fMRI data [168],genome-wide causal variants study [167], triggering relation discovery on cyber security [170], clinical diagnose and treatment [161,169], stock market in Shanghai [140],Spanish mining accident [146], industrial occupational safety [171], the Titanic data set, the adult data set census income and 5 groups of synthetic data set [147], SemEval-2010-Task8 dataset [177] fast algorithm to discover causal signals in large-scale data set especially when the target or outcome variable is fixed, mining and selecting optimal parameters for further causal analysis modelling, identify the causes of failures in large Internet sites, demonstrate human volitionally regulate hemodynamic signals from circumscribed regions of the brain leading to area-specific behavioral consequences, identify genetic variants associated with disease, determine classification model to help on obtaining efficient decision for treating cancer patients.…”

Section: Associationmentioning

confidence: 99%

“…Furthermore, Lee et al [168] applied SVM technique in medical study on the fMRI data to observe changes in the spatial activation patterns in the brain across the training sessions, so to be prepared for the implementation of multivariate Granger causality modelling to calculate directed causal influences between spatially distributed voxels of the brain. The authors in [169] also focused on the medical studies aspect and conducted the study to extract causality patterns for the problemaction relations in discharge summaries so to present a chronological view of a patients' problem and an doctor's action. In which, the causal relationship between events from clinical narratives are investigated and the clinical semantic unit is classified by adopting SVM.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Big Data and Causality

2017

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Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of Big Data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among Big Data has dramatically increased. Data Mining, the process of uncovering hidden information from Big Data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in Big Data. To this end the paper reviews recent significant applications of Data Mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of Data Mining applications in causality analysis domain as a reference directory.

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Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries

Cited by 18 publications

References 21 publications

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

Deep Learning based Information Extraction Framework on Chinese Electronic Health Records

Big Data and Causality

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